Prolog is a logic programming language based on first-order predicate logic. Some key points:
- Prolog programs consist of facts and rules defined with predicates and clauses. Predicates take arguments that can be variables, constants, or terms.
- Prolog is declarative rather than procedural - programs specify relations between objects rather than algorithms or steps. Prolog uses backtracking to try different substitutions for variables to determine if queries are true.
- Prolog was invented in the 1970s and was inspired by logic programming research. It is well-suited for symbolic processing, knowledge representation, and natural language processing.
What one needs to know to work in Natural Language Processing field and the aspects of developing an NLP project using the example of a system to identify text language
Formal and Computational Representations
The Semantics of First-Order Logic
Event Representations
Description Logics & the Web Ontology Language
Compositionality
Lamba calculus
Corpus-based approaches:
Latent Semantic Analysis
Topic models
Distributional Semantics
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Java is not purely object oriented language but to say hybrid language because java not support complete OOP concept. It also called java is primitives programming language.
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An introductory course for Ontology Engineering using Controlled Natural Language. Fluent Editor (FE) is an ontology editor that is a tool for editing and manipulating ontologies. The main feature of Fluent Editor is that it uses controlled natural language (CNL) to communicate with a user. Communication with CNL is a more suitable for human users alternative to XML-based OWL editors.
Prolog, Prolog Programming IN AI. Prolog is an Artificial Intelligence programming language. Prolog is a logic programming language associated with artificial intelligence and computational linguistics. Its applications include natural language understanding and expert systems. Prolog is notably a so-called nonprocedural, or declarative, language.
What one needs to know to work in Natural Language Processing field and the aspects of developing an NLP project using the example of a system to identify text language
Formal and Computational Representations
The Semantics of First-Order Logic
Event Representations
Description Logics & the Web Ontology Language
Compositionality
Lamba calculus
Corpus-based approaches:
Latent Semantic Analysis
Topic models
Distributional Semantics
Why Java is not a purely object oriented language?NexSoftsys
Java is not purely object oriented language but to say hybrid language because java not support complete OOP concept. It also called java is primitives programming language.
Introduction to Ontology Engineering with Fluent Editor 2014Cognitum
An introductory course for Ontology Engineering using Controlled Natural Language. Fluent Editor (FE) is an ontology editor that is a tool for editing and manipulating ontologies. The main feature of Fluent Editor is that it uses controlled natural language (CNL) to communicate with a user. Communication with CNL is a more suitable for human users alternative to XML-based OWL editors.
Prolog, Prolog Programming IN AI. Prolog is an Artificial Intelligence programming language. Prolog is a logic programming language associated with artificial intelligence and computational linguistics. Its applications include natural language understanding and expert systems. Prolog is notably a so-called nonprocedural, or declarative, language.
Foundations of Knowledge Representation in Artificial Intelligence.pptxkitsenthilkumarcse
Knowledge representation in artificial intelligence (AI) is a fundamental concept that involves the process of structuring and encoding knowledge so that AI systems can understand, reason, and make decisions. Effective knowledge representation is essential for AI systems to model and work with complex real-world information. Here are some key aspects of knowledge representation in AI:
Symbolic Knowledge Representation: This approach uses symbols and rules to represent knowledge. It involves encoding information using symbols, predicates, and logical statements. Common formalisms include first-order logic and propositional logic. Symbolic representation is particularly suited for knowledge-based systems and expert systems.
Semantic Networks: In a semantic network, knowledge is represented using nodes and links to denote relationships between concepts. This form of representation is intuitive and is often used for organizing knowledge in a structured manner.
Frames and Ontologies: Frames and ontologies are used to represent knowledge by structuring information into frames or classes. Frames contain attributes and values, and they help in organizing and categorizing knowledge. Ontologies, such as OWL (Web Ontology Language), provide a more formal representation of knowledge for use in the semantic web and knowledge graphs.
Rule-Based Systems: Rule-based systems use a set of rules to represent and reason with knowledge. These rules can be encoded in the form of "if-then" statements, allowing AI systems to make decisions and draw inferences.
Fuzzy Logic: Fuzzy logic allows for the representation of uncertainty and vagueness in knowledge. It is particularly useful in situations where information is not black and white but falls within degrees of truth.
Bayesian Networks: Bayesian networks represent knowledge using probability distributions and conditional dependencies. They are valuable for modeling uncertain or probabilistic relationships in various domains, such as medical diagnosis and risk analysis.
Connectionist Models: Connectionist models, like neural networks, use distributed representations to encode knowledge. In these models, knowledge is spread across interconnected nodes (neurons), and learning occurs through the adjustment of connection weights. These networks are particularly effective in tasks such as pattern recognition and natural language processing.
Hybrid Approaches: Many AI systems use a combination of different knowledge representation techniques to address the complexities of real-world problems. For instance, combining symbolic representation with connectionist models is a common approach in modern AI.
The choice of knowledge representation method depends on the specific problem domain, the nature of the data, and the requirements of the AI system.
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It has a simple design that encourages code readability and supports multiple programming models.
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Python is ideal for data mining and considerable data facilitation.
Lecture slides by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2011/09/knowledgeengineering-fall2011.html
and http://www.jarrar.info
and on Youtube:
http://www.youtube.com/watch?v=3_-HGnI6AZ0&list=PLDEA50C29F3D28257
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
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The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
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Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
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Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
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Antifertility, Toxicity studies as per OECD guidelines
1. PROLOG
P R I N C I P L E S O F P R O G R A M M I N G L A N G U A G E S
2. PRESENTED BY:
H A S S A A N A H M A D ( 0 7 )
A Z E E M A S H R A F ( 1 4 )
M U H A M M A D U S A M A I Q B A L ( 1 5 )
S Y E D B A Q A R A B B A S ( 1 9 )
3. WHAT IS PROLOG?
• Prolog stands for programming in logic (PROgrammation en LOGique).
• Prolog is the most widely used language to have been inspired by logic
programming research.
• Prolog is the only successful example of the family of logic
programming languages.
4. CONT..
• Prolog is a declarative programming language.
• In the case of prolog, it will tell you whether a prolog sentence is true or not.
• if it contains variables, what the values of the variables need to be.
• Prolog is declarative. A Prolog programmer concentrates on what the program
needs to do, not on how to do it.
5. A LITTLE HISTORY
• Prolog was invented by Alain Colmerauer, a professor of computer science at
the university of Aix-Marseille in France, in 1972.
• The first application of Prolog was in natural language processing.
• Its theoretical underpinning are due to Donald Loveland of Duke university
through Robert Kowalski (formerly) of the university of Edinburgh
6. ANATOMY OF PROLOG PROGRAM
• Prolog programs are made up of facts and rules.
• A fact asserts some property of an object, or relation between two or more
objects.
e.g. parent(jane,alan).
Can be read as “Jane is the parent of Alan.”
• Rules allow us to infer that a property or relationship holds based on
preconditions.
e.g. parent(X,Y) :- mother(X,Y).
= “Person X is the parent of person Y if X is Y‟s mother.”
7. DATA TYPES
• Prolog single data type is the term. Terms are either atoms, numbers or
variables.
• An Atom is a general-purpose name with no inherent meaning.
Examples of atoms include
– X
– Red
– Taco
• Numbers can be floats or integers.
• Variables are denoted by a string consisting of letters, numbers and
underscore characters, and beginning with an upper-case letter or
underscore.
8. WHY LEARN PROLOG?
• Learning prolog will certainly not always be as fruitful as learning C++,
Java or Python.
• Prolog is most useful in the areas related to Artificial Intelligence
research, such as problem solving, (path) planning or natural language
interpretation.
• As prolog uses certain programming techniques that are considered
difficult or advanced
• prolog programmer will certainly have an in-depth understanding of
such concepts as recursion, searching problem trees etc.
9. CONT..
• Very complex programs have been written in prolog.
• Most of all prolog is a complex, powerful, and elegant language that
can inspire great satisfaction in its users.
• learning prolog is not easy because Prolog doesn't work as naturally as
procedural languages and therefore requires a specific way of thinking.
10. INTRODUCTION TO LOGIC
• Since Prolog is based heavily on formal logic, it's useful to have some
experience with it.
• There are two types of logical languages:
– propositional logic
– first-order logic.
11. PROPOSITIONAL LOGIC
• Propositional logic has two basic elements:
– terms
– Connectives
• Terms are represented by letters (usually upper-case), and represent
the values true and false.
• Connectives, like the word suggests, connect two terms.
• Connectives are usually represented by symbols rather than words.
12.
13. FIRST ORDER LOGIC
• First Order Logic (also known as predicate logic) expands on
propositional logic, by using predicates, variables and objects.
• In first order logic the atomic sentences are predicates.
• Predicates have a name (starting with a capital) which is followed
by several variables (starting with a lowercase letter).
14. CONT..
• The following are examples of predicates:
– Predicate(variable1, variable2)
– BrotherOf(sibling, brother)
– MotherOf(child, mother)
– HasWheels(thing)
• these variables can be instantiated with objects.
15. CONT..
• Objects are elements represented by words that start with a capital
letter.
• Such a collection of sentences that are true is called a knowledge base.
• Such a knowledge base could, for instance contain the following
sentences:
– HasWheels(Car)
– MotherOf(Charles, Elizabeth)
• The sentences tell us that the HasWheels predicate is true
16. PREDICATE DEFINITIONS
• Both facts and rules are predicate definitions.
• ‘Predicate’ is the name given to the word occurring before the bracket in
a fact
or rule:
– parent (jane,alan).
• By defining a predicate you are specifying which information needs to
be
known for the property denoted by the predicate to be true.
17. CLAUSES
• Predicate definitions consist of clauses.
• An individual definition (whether it be a fact or rule).
e.g. mother(jane, alan). = Fact
parent(P1,P2):- mother(P1,P2). = Rule
• A clause consists of a head And sometimes a body.
• Facts don't have a body because they are always true.
• Clauses with empty bodies are called facts.
• Clauses with bodies are called rules.
18. ARGUMENTS
• A predicate head consists of a predicate name and sometimes some
arguments
contained within brackets and separated by commas.
mother(jane, alan).
Predicate name Arguments
• A body can be made up of any number of subgoals (calls to other
predicates) and terms.
19. CONT..
• Arguments also consist of terms, which can be:
– Constants e.g. jane,
– Variables e.g. Person1, or
– Compound terms
21. CONSTANTS
Constants can either be:
• Numbers:
– integers are the usual form (e.g. 1, 0, -1, etc), but
– floating-point numbers can also be used (e.g. 3.9)
• Symbolic constants:
– always start with a lower case alphabetic character and contain any
mixture of
letters, digits, and underscores (but no spaces, punctuation, or an initial
capital).
– e.g. abc, big_long_constant, x4_3t.
• String constants:
22. VARIABLES
• Variables always start with an upper case alphabetic character or an
underscore.
• Other than the first character they can be made up of any mixture of letters,
digits, and underscores.
e.g. X, ABC, _89two5, _very_long_variable
• There are no “types” for variables (or constants) – a variable can take any
value.
• All Prolog variables have a “local” scope:
They only keep the same value within a clause; the same variable used
outside
of a clause refers as “global” scope.
23. NAMING TIPS
• Use real English when naming predicates, constants, and variables.
– e.g. “John wants to help Somebody.”
– Could be: wants(john, to_help , Somebody).
– Not: x87g(j,_789).
• Use a Verb-Subject-Object structure:
– wants(john, to_help).
• BUT do not assume Prolog Understands the meaning of your chosen
names
25. After SWI-Prolog has been installed on a Windows system, the following
important new things are available to the user:
• A folder (in program files) called swipl containing the executable, libraries, etc.,
of the system.
• No files are installed outside this directory.
• Program swipl-win.exe, providing a window for interaction with Prolog.
• The program swipl.exe is a version of SWI-Prolog that runs in a console
window.
• The file extension .pl is associated with the program swipl-win.exe.
26. EXECUTING A QUERY
After loading a program, one can ask Prolog queries about the program.
?- likes (sam, x) .
X = biryani ;
X = tandoori ;
……
X = chips ;
?-
27. PROLOG EXECUTION
• Most Prolog clauses have both a declarative reading and a procedural
reading.
• Whenever possible, the declarative reading is to be preferred.
– mother (X, Y) :- parent (X, Y), female (X) .
• Declarative reading: x is the mother of y
– if x is parent of y and x is female
28. CONT..
Procedural reading :
• To show that x is the mother of y, first show that x is a parent of y, then
show that x is female.
Clauses:
oparent (john, bill) .
oparent (jane, bill) .
ofemale(jane) .
Query:
o| ?- mother (M, bill) .
29. CONT..
• The clause of mother will be checked, and the unification X=M, Y=bill will occur.
• Then parent (M, bill) will be attempted, resulting in the unification M=john.
• Next, female (john) will be attempted, but will fail.
• Prolog will backtrack to parent (M, bill) and look for another solution for this; it will
succeed and unify M=jane.
• Finally, it will attempt female (jane), and succeed; so the inquiry will succeed,
having performed the unification M=jane.
30. ADVANTAGES
• Ease of representing knowledge.
• Natural support of pattern-matching.
• Natural support of meta-programming.
• Meaning of programs is independent of how they are executed.
• Simple connection between programs and computed answers and
specifications.
• No need to distinguish programs from databases.
31. LIMITATIONS
• Although Prolog is widely used in research and education, Prolog and other logic
programming languages have not had a significant impact on the computer
industry in general.
• Most applications are small by industrial standards, with few exceeding 100,000
lines of code.
• Programming in the large is considered to be complicated because not all Prolog
compilers support modules
• because there are compatibility problems between the module systems of the
major Prolog compilers.
• Prolog is not purely declarative because of constructs like the cut operator,
• a procedural reading of a Prolog program is needed to understand it.
32. EXTENSIONS
• Various implementations have been developed from Prolog to extend logic
programming capabilities
• these include:
– types,
– modes,
– constraint logic programming (CLP),
– object-oriented logic programming (OOLP),
– linear logic (LLP),
– functional and higher-order logic programming capabilities,
– interoperability with knowledge bases
33. TYPES
• Prolog is an untyped language.
• Attempts to introduce types date back to the 1980s and as of 2008
there are still attempts to extend Prolog with types.
34. MODES
• The syntax of Prolog does not specify which arguments of a predicate
are inputs and which are outputs.
• Modes provide valuable information when reasoning about Prolog
programs and can also be used to accelerate execution.
37. GRAPHICS
• Prolog systems that provide a graphics library are:
– SWI-Prolog
– Visual Prolog WIN-PROLOG
– B-Prolog
• SWI also support server-side web programming with support for web
protocols, HTML and XML.